Graybill and Idso [1993] Online

A lot of people who are interested in the issue of the effect of bristlecone pines on MBH98 do not have access to Graybill and Idso [1993], the underlying reference, which we referred to in both MM05 articles and which I mentioned in this post . Here’s a pdf version , courtesy of the U.S. Water Conservation Lab. I was really amazed when I connected the MBH98 PC1 to the Graybill sites and, when you read Graybill and Idso [1993], I think that you will be as well. As we’ve mentioned before, there is extraordinary irony that Sherwood Idso’s data should now be relied upon by Mann et al. as essential to their temperature reconstruction. Every single expedient presented by them, whether couched in Preisendorfer’s Rule N or other lofty talk about “significance”, has one single purpose: to get Idso’s bristlecone pine growth series into their calculation. pdf version

47 Comments

Now that I see it in its original context, the implication is very clear: the bristlecone pines react strongly to carbon dioxide fertilization compared to other species. The hockeystick is clearly an artifact of the BPs. Nothing else.

That’s WHY they’re so important to MBH and why Mann is hugging them so much.

I’m responding to Phil’s questions in this thread. I am reading the paper (I think for the first time). Hard to really concentrate and focus. It is an ok paper, I guess. Suggestive maybe. I have some specific concerns/questions, but need to parse it a bit harder.

Ok, I’m really tired, so I may add some more thoughts or remove some questions. (My mind wandered a bit as I read.)

a. It’s a decent paper. Suggestive. Attempts to be thoughtful. Still a lot of different confounding issues and limited data sets, so the explanation while interesting, is not compelling.

b. Wasn’t sure what was supposed to be so great about page 89. Figure 5 is interesting to show the correlation (and divergence) of strip and full bark specimens. But the figure does not show that CO2 fertilization is the cause.

c. The pearson numbers are helpful though. And kudos for trying to look at confounding factors. But I did not look at them closely, nor am I am I expert enough to really give them a good sniff test. I wonder why poeople don’t just do conventional multiple regressions?

d. I would like to see more about the sour tree experiment. Also, despite the comments in the intro, it’s not clear to me that the Idso sour trees are the best FACE experiments. Also not clear to me how well sour trees will compare to bcps.

e. Wonder why the bcps and sour oranges show such CO@ fertilization and other tree ring chronologies do not. Shouldn’t we have CO@ fertilization all over the world? WHat is particular botanically about bcps to make them such sensitive CO2 detectors?

f. Not clear to me what is going on in figure 4. The series (sour orange and respective strip bark form) seem to match perfectly at both beginning and end. Were they fixed at more then one end? If that is the case, what can the graph tell us about correlation? Also, what is the botanical explanation of why one form matchs the cambium curve (of orange) and the other matches the overall biomass. That seems like a strain. Why should RW of one tree species correspond to total biomass add of another?

g. The comments about roots, would be better if led to an experiment. Seems like it would be easy to dig up some tree roots and prove one way or other.

h. The paper deserves some CA panning since it does not address either grazing or dry lake bed fertilizing. (evil grin.)

i. Did this lead anywhere? ARe there follow studies to refute or extend it?

TCO, I wasn’t addressing the CO2 or the exact reasons for the divergence, but my question was which of the two series (or neither) would you use for a temperature proxy. You were suggesting the sharp trend of the strip-bark could possibly be due to temperature, but that would suggest that the full-bark was no longer temperature responsive, since they had track well before 1870.

I guess you can make some argument that if they diverged that one can use one or the other as proxies, but not both. However, the approach of Mann was designed to use a lot of different series and acknowledged (implicitly) that some would have confounding factors.

I still think the correlation and then deviation is very interesting. It would be nice to resolve if the difference is from CO2, temperature, grazing, dry-lakebed-fertilizing, or chance.

Re #8, TCO, I live about 100 miles to the south of Sheep Mt, so I can attest first hand to the Owen Lake bed dust storms. With a strong north wind blowing, it appears like a tornado has touched down. I have also visited both Sheep Mt and Cirque Peak areas, I believe at these high elevations that sheep grazing probably wasn’t a player, too many nice meadows at lower elevations close to these sites. Temperature, in my view seems to be the poorest choice as they tracked nicely until 1870, then the strip-bark tree diverges but the full-bark doesn’t exceed previous years peaks. It is hard to argue a reduced temp sensitivity for the full-bark for last century, unless one could argues the age detrending wasn’t done properly for the full-bark or admit to nonlinear temperature response. Having seen first hand and many photos of the bcp, the strip-bark trees seem to live a very tortured existence. Personally, the correlation to local and grid box annual temperature is so poor and with the known other issues, none of tree rings should be used.

You are on stronger ground arguing that the series don’t correlate to gridcells, then to imply that the divergence shows temp not a player. For this reason: If (per Idso) one can assume that one sort responds more to CO2 then the other (note no botanical rationale given for that), then one can assume that one responds to temp and the other doesn’t. And since we know that temp increase and CO2 increase occur together, how can one say “it’s CO2”, when as easily it could be temp. So, you’re better off arguing the gridcell thing.

Re #11 TCO, The White Mountain Research Center (WMRC) has two weather stations relatively close to Sheep Mountain. Barcroft and Crooked Creek are their names, and the WMRC website has some older weather data on line. WMRC originally studied high altitude biological effects and now have reinvented themselves to study (guess what) Climate Change. Following the money.

RE: #11 – Add in something about where in the weathering life cycle of the Bishop Tuff measurements apparently correspond to. In other words, did the weathering of the Bishop Tuff reach a certain stage ~ 150 or so years ago, and are new chemistries resulting?

You’d think that the research would be done before Mann introduced bristlecones into climate reconstructions rather than afterwards. BTW Dano, any news on Hughes’ Sheep Mountain results from 2002? What’s the news from listserv? Or did he have “bad” results?

Steve M,
It’s a bit like the trend analysis issue over in the hurricane thread. You’d think if one were going to make sweeping claims about unprecedented trends in some stochastic dynamic process that you would read a basic textbook about time-series analysis, to understand how basic concepts like “sampling error” apply in the tricky case of continuously evolving thermodynamic processes. That warmers don’t read these books is not surprising to me (I have “priors”), but should be astonishing to outsiders and policy-makers.

A. Agreed, lots of things that Mann should have done and that would still be interesting to do now.

B. However, it’s not clear to me that the bcp “situation” (in terms of possible confounding factors) is any worse then other set of proxy tree series. I have not seen any demonstration of this from you, Steve. This would better back up your arguments for throwing out bcps (but keeping the rest).

UC, I visited the page, as requested, and saw no link to any pdf. Maybe i missed something? At any rate, wading around in webworld is a waste of my time and Steve M’s bandwidth. If you really want someone to read a paper, you have to make it easy for them. You know how many requests I get to review papers each day? Too many.

Actually that Mann & Lees paper is useless. Their conclusion states:
“Our procedure uses multiatepr spectral estimation in concert with a robust noise level determination to allow for optimal detection of periodic and quasi-periodic signals buried in cliamtic red noise”
when the real problem is much more challenging than that. The real challenge, which is impossible by statistical means, is to distinguish all-scale signals (not just the periodic ones) from 1/f noise.

This is a fair characterization of the the situation in climatology, where one person’s signal is another person’s noise. Note how the noise vs. signal dichotomy is far more meaningful in dendroecology, where the tree is acting as a non-chaotic linear filter.

The real challenge, which is impossible by statistical means, is to distinguish all-scale signals (not just the periodic ones) from 1/f noise.

I think they show in Chap. 4 that they can distinguish all kinds of signals from the AR1-noise background. But they don’t show what happens in the noise-only case (p=0.93, for example). And what if the ‘signal’ is AR1? (sorry, Ritson coeff again..). Then the signal+noise is AR1+AR1=ARMA(2,1). Can you distinguish that from ARMA(2,1)-noise-only case?

Note how the noise vs. signal dichotomy is far more meaningful in dendroecology, where the tree is acting as a non-chaotic linear filter.

Yes, in that case you can separate signal and noise by controlled experiment: place a thermometer next to a tree. Thermometer reading is the signal and ‘tree-reading’ is the signal+noise. Then you can obtain models for both signal and noise. But we can’t do controlled experiments like ‘Earth without CO2’, so here we are dealing with this strange ‘background noise’

Don’t you hate the way these guys use the word "robust"? The other word that bugs me is the generic use of "rigorous", as in the recent US CCSP report – "we use rigorous statistical methods". Whenever I see Wigley or Santer or such use the word "rigorous", I make sure that my pocket hasn’t been picked.

1. “Robust”. Yes, I hate (and hate is a strong word) the way these guys use the word. It physically, measurably pains me. Robust means “doesn’t fall to pieces when you poke it a little bit”.
2. “Rigorous”. Can someone tell me: what are non-rigorous statistical methods, and who would admit to using them? I guess the phrase could be meaningful in context. But taken out of context it sure sounds silly.

I think you use the word in its correct statistical sense, but still rely a bit on the connotation to help your “fight”. I’d like your response to the general issue I raised about the problems with removing outliers. I think this is a rich area and you have not given a thorough discussion of it. Sure, it’s interesting that the results change when you remove an outlier. Sometimes you should. Other times, you shouldn’t. This is a non-trivial issue and I think you are a bit breezy here.

BCPs are not “outliers” in the statistical sense of the term. What they are is a mystery. Testing the effect of striking out observations you can not explain is not only reasonable, it is an obligation.

I agree. They just drop down the confidence levels (compare Fig. 8b and 8c) so that trends become significant. And they dont run a test with p=0.93 noise only.. Why?

# 33

If you design a linear filter based on Gaussian noise assumption (very easy), and then find out that there are large errors in the output, the easiest way to continue is to remove the ‘outliers’ (if the system is overdetermined). Sometimes it works and sometimes it doesn’t work. (signal processing point of view)

33. BCPs are outliers in the metric of “hockey stick index”. (Aren’t they?)

I have no problem with testing the sensitivity of the reconstruction to removing a part of the data set. I just want to go further and discuss said sensativity, what it means, how important it is, etc.

#33. TCO, The whole topic of outliers and “robust statistics” – using “robust” as Hampel and statisticians use it rather than as a term of self-approval as Mann uses it – is a big and complicated topic. TRy articles by Hampel or Huber and you’ll see why.

The take-home message from any perusal of the more accessible papers in robust statistics literature is that methods and data issues are inter-related – a point that you struggle against in constantly harping about whether something is a point about data or methods. As Hampel discusses things, you use methods to identify “leverage” points/outliers and then have to decide on scientific (rather than a priori statistical grounds) whether such leverage points should be part of the analysis.

The high leverage of BCPs was discovered through seat-of-the-pants methodology (Seeing what the crazy MBH principal comopnents method did) rather than through a high-powered method, but, once identified as high-leverage, it doesn’t matter any more how the leverage was identified. The question is whether there is a scientific basis for treating them as outliers. Here one reasonably relies on testimony of the specialists, most recently, Biondi’s chapter in the NAS panel.

Far from regarding our approach in EE as entangling methodological and data issues, I think that this was a completely appropriate approach to disentangling things on a practical basis. TOday I’d frame it in more high-falutin terms and cite Hampel, but the practical effect is the same.

I would think to any reasonable person the fact that removal of a couple of data point changes the story is enough to throw out all studies that rely on them for their conclusion. It doesn’t matter if they are 5000 years old or like CO2 or whatever, a cautious person would be very concerned with studies suggesting world policy should be based on just a couple of trees. Having the NAS backing is nice, but a sad reflection that its necessary.

You use the term robust in it’s proper meaning, but seem to devolve to the “connotation” level when remarking on non-robustness. My point is how bad is it to be non-robust? As you referenced, this is a complicated issue (when to take data out of a set, when it is relevant to expect performance with data out of the set). My point is that since it is non-trivial, the onus is on you to extend your argument more if you want to say that the non-robustness is bad, rather then just to note the point itself.

Biondi/NAS is crap in terms of explication of bcp proxy effectiveness. You are playing BS debate games, not doing science (or even good business analysis) in citing an expert rather then an argument. And in this case, since you know the expert hasn’t looked at the problem in detail and even (NAS panel) has a history of making unsupported, wrong, unexamined statements, your comment is tendentious.

Steve, you, definetely you, are the one who is having a problem with issue analysis. If there is a problem with an interaction of data and methods, fine. But you have not clearly defined if your critique has multiple legs of a stool supporting it (independant errors in MBH) or if it requires everything to “hang together” to buttress your critique. It is trivial to consider method change, data change and (method and data change) as variables to consider in terms of effect on the reconstruction. Look at BC05 and how he does a full factorial. And when we go to examine a specific issue, you wiggle and want to draw in other issues. If I directly ask a question on the effect of X28 (in isolation), then that is the question which I’m asking. Responding by trying to shift the discussion to X31 or to the interaction of X28 and X31 is non-responsive and evasive.

Awww. Its silly. Its like, OK Mann made chocolate cake. If I vary all the ingredients I get a range of other stuff, only some of which is chocolate cake. So… maybe Mann could have made something else. The response is — analytical decisions are not neutral. And then you get into endless complex arguments about the best way to make chocolate cake. Waste of time IMHO.

Let’s say that I have a thesis that the high cost of the war in Iraq is related to Haliburton’s KBR business unit. Those bcp-loving, Cheney-led privateers! I go do some analysis and find out that KBR has increased its percentage of the work (among contractors) from 5% to 50%, I find that in the general market, they are only 10% of the overall, but in Iraq work, that they are…50%. In addition, I find a long litany of overcharges that they’ve had on projects. Sounds like KBR (Haliburton) is the problem, no? No.

Now I go and examine KBR in comparison to other contractors in Iraq, to the general market for contractors. And I find that they’re all about just as bad. Thus I find that the issue of KBR’s specific issues, of their “loading” was a distraction. The fundamental issue is something about government contracting overall.

This seems like a simple and relevant concept to check on, no? Just like knowing how mining affects the hockey stick itself, rather then an intermediate is a relevant issue also, no? Why is there so much resistance to considering things fairly on a site that is supposed to be about fair analysis?

Well, I think your argument is that perhaps all tree rings don’t correlate well with temperature or have some other problem would be cause for concern too. But that doesn’t mean BCPs shouldn’t be picked on does it? If you throw out BCPs, highlight divergence, the authority of treerings to climate change fades, and dendro people go back to what they were doing before, perhaps including the type basic analysis you suggest.